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A new form of mobile payment, Quick Response (QR) code, has been a popular way of paying bus fares in China since 2017. Compared with conventional payment methods, cash or IC card, QR code shows a lot of differences in response time, recognition accuracy, and payment procedure, which significantly influences the boarding service time (BST) for passengers. However, no research has considered its efficiency. This study, therefore, tries to fill this gap and investigate its influence on BST. Sufficient ride-check data were collected, and the influence of the QR code payment method on BST was examined through a set of regression models. Passengers pay the bus fare with different payment methods as their first choice; nevertheless, when the payment fails, they may transfer among them. According to the payment choice, result, and process, we introduce the first-choice-based, the last-choice-based, and the choice-transfer-based models, respectively. The scenario with delays in calling out the QR code was considered in the choice-transfer-based model. The onboard crowdedness was regarded as a categorical variable to determine the regime of the boarding process in all models. We conduct empirical analysis in Wuhan, and this study can help to identify the influence of the QR code payment method on BST, consequently, improving bus service efficiency., 1. Introduction The smartphones have changed our daily life since they were invented in 1993 when IBM developed the first touchscreen phone named Simon with the Zaurus operating system. They [...]

Gas adsorption experiments are becoming one of the most common methods to quantify and analyze the pore structures of shale samples in the petroleum industry. In this regard, particle size of the specimen plays an important role in the results that could ultimately affect the pore structure interpretation. Hence, in this study, five shale samples at different thermal maturity levels are picked, and all are crushed into different groups of particle sizes: less than 40 mesh (<375 μm), less than 60 mesh (<250 μm), less than 80 mesh (<187.5 μm), and less than 100 mesh (<150 μm). Next, N2 adsorption is used to characterize the pore structures of the samples within different particle sizes. Furthermore, to interpret the data, several attributes such as the pore volume, surface area, fractal dimension (from the fractal analysis), and heterogeneity index (from the multifractal analysis), are studied and compared between the samples and particle size intervals to provide us with the effect that particle size could have on the pore structure analysis. The results showed that as the particle size varies, the pore structures of the shale samples could get affected. Based on the comparison of the results, it is recommended that a suitable particle size for the shale pore structure characterization in N2 adsorption experiments should be less than 60 mesh (<250 μm). [ABSTRACT FROM AUTHOR]